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<div class="header">
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<a href="#pub-types">Public 类型</a> &#124;
<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pri-methods">Private 成员函数</a> &#124;
<a href="#pri-attribs">Private 属性</a> &#124;
<a href="classpcl_1_1filters_1_1_pyramid-members.html">所有成员列表</a>  </div>
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<div class="title">pcl::filters::Pyramid&lt; PointT &gt; 模板类 参考</div>  </div>
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<p><code>#include &lt;<a class="el" href="pyramid_8h_source.html">pyramid.h</a>&gt;</code></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public 类型</h2></td></tr>
<tr class="memitem:a8ff3408be209480db832a9b15d296e3a"><td class="memItemLeft" align="right" valign="top"><a id="a8ff3408be209480db832a9b15d296e3a"></a>
typedef <a class="el" href="classpcl_1_1_point_cloud.html">PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudPtr</b></td></tr>
<tr class="separator:a8ff3408be209480db832a9b15d296e3a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1ed38856f69e8527f6e6e39c6d50546c"><td class="memItemLeft" align="right" valign="top"><a id="a1ed38856f69e8527f6e6e39c6d50546c"></a>
typedef <a class="el" href="classpcl_1_1_point_cloud.html">PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::ConstPtr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudConstPtr</b></td></tr>
<tr class="separator:a1ed38856f69e8527f6e6e39c6d50546c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab89c41ef80f239b25b1cfe3b697cac9a"><td class="memItemLeft" align="right" valign="top"><a id="ab89c41ef80f239b25b1cfe3b697cac9a"></a>
typedef boost::shared_ptr&lt; <a class="el" href="classpcl_1_1filters_1_1_pyramid.html">Pyramid</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>Ptr</b></td></tr>
<tr class="separator:ab89c41ef80f239b25b1cfe3b697cac9a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aaf5dea76f99ee8b042889fd687ac542d"><td class="memItemLeft" align="right" valign="top"><a id="aaf5dea76f99ee8b042889fd687ac542d"></a>
typedef boost::shared_ptr&lt; const <a class="el" href="classpcl_1_1filters_1_1_pyramid.html">Pyramid</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>ConstPtr</b></td></tr>
<tr class="separator:aaf5dea76f99ee8b042889fd687ac542d"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public 成员函数</h2></td></tr>
<tr class="memitem:a49d26b9d8b7b30166aba459fe9c1170f"><td class="memItemLeft" align="right" valign="top"><a id="a49d26b9d8b7b30166aba459fe9c1170f"></a>
&#160;</td><td class="memItemRight" valign="bottom"><b>Pyramid</b> (int levels=4)</td></tr>
<tr class="separator:a49d26b9d8b7b30166aba459fe9c1170f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa05721343da8b52426dcb1b704452e02"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#aa05721343da8b52426dcb1b704452e02">setInputCloud</a> (const PointCloudConstPtr &amp;cloud)</td></tr>
<tr class="memdesc:aa05721343da8b52426dcb1b704452e02"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the input dataset  <a href="classpcl_1_1filters_1_1_pyramid.html#aa05721343da8b52426dcb1b704452e02">更多...</a><br /></td></tr>
<tr class="separator:aa05721343da8b52426dcb1b704452e02"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6190a9ba01f9e164313f2f7f377f6c88"><td class="memItemLeft" align="right" valign="top"><a id="a6190a9ba01f9e164313f2f7f377f6c88"></a>
PointCloudConstPtr const&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#a6190a9ba01f9e164313f2f7f377f6c88">getInputCloud</a> ()</td></tr>
<tr class="memdesc:a6190a9ba01f9e164313f2f7f377f6c88"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the input point cloud dataset. <br /></td></tr>
<tr class="separator:a6190a9ba01f9e164313f2f7f377f6c88"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1421311278e5d90a4cb936a88ff79cb8"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#a1421311278e5d90a4cb936a88ff79cb8">setNumberOfLevels</a> (int levels)</td></tr>
<tr class="memdesc:a1421311278e5d90a4cb936a88ff79cb8"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the number of pyramid levels  <a href="classpcl_1_1filters_1_1_pyramid.html#a1421311278e5d90a4cb936a88ff79cb8">更多...</a><br /></td></tr>
<tr class="separator:a1421311278e5d90a4cb936a88ff79cb8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0f827772542d5fa4df7210608972f433"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#a0f827772542d5fa4df7210608972f433">getNumberOfLevels</a> () const</td></tr>
<tr class="separator:a0f827772542d5fa4df7210608972f433"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a708555045622338b43cf7a1439ce6d03"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#a708555045622338b43cf7a1439ce6d03">setNumberOfThreads</a> (unsigned int nr_threads=0)</td></tr>
<tr class="memdesc:a708555045622338b43cf7a1439ce6d03"><td class="mdescLeft">&#160;</td><td class="mdescRight">Initialize the scheduler and set the number of threads to use.  <a href="classpcl_1_1filters_1_1_pyramid.html#a708555045622338b43cf7a1439ce6d03">更多...</a><br /></td></tr>
<tr class="separator:a708555045622338b43cf7a1439ce6d03"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a85795a7ea853143678a8ad02a2aae296"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#a85795a7ea853143678a8ad02a2aae296">setLargeSmoothingKernel</a> (bool large)</td></tr>
<tr class="memdesc:a85795a7ea853143678a8ad02a2aae296"><td class="mdescLeft">&#160;</td><td class="mdescRight">Choose a larger smoothing kernel for enhanced smoothing.  <a href="classpcl_1_1filters_1_1_pyramid.html#a85795a7ea853143678a8ad02a2aae296">更多...</a><br /></td></tr>
<tr class="separator:a85795a7ea853143678a8ad02a2aae296"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a69d206f5d132cc93c8ec8889f2106d04"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#a69d206f5d132cc93c8ec8889f2106d04">setDistanceThreshold</a> (float threshold)</td></tr>
<tr class="separator:a69d206f5d132cc93c8ec8889f2106d04"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a32e8b1516818794aabad33ab343cdca5"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#a32e8b1516818794aabad33ab343cdca5">getDistanceThreshold</a> () const</td></tr>
<tr class="separator:a32e8b1516818794aabad33ab343cdca5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1685b0b45e15390522d88c1914fbcfb8"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#a1685b0b45e15390522d88c1914fbcfb8">compute</a> (std::vector&lt; PointCloudPtr &gt; &amp;output)</td></tr>
<tr class="memdesc:a1685b0b45e15390522d88c1914fbcfb8"><td class="mdescLeft">&#160;</td><td class="mdescRight">compute the pyramid levels.  <a href="classpcl_1_1filters_1_1_pyramid.html#a1685b0b45e15390522d88c1914fbcfb8">更多...</a><br /></td></tr>
<tr class="separator:a1685b0b45e15390522d88c1914fbcfb8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9aaf4a6a3fefe67124221ae38c903a00"><td class="memItemLeft" align="right" valign="top"><a id="a9aaf4a6a3fefe67124221ae38c903a00"></a>
const std::string &amp;&#160;</td><td class="memItemRight" valign="bottom"><b>getClassName</b> () const</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>compute</b> (std::vector&lt; <a class="el" href="classpcl_1_1filters_1_1_pyramid.html">Pyramid</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b.html">pcl::PointXYZRGB</a> &gt;::PointCloudPtr &gt; &amp;output)</td></tr>
<tr class="separator:a45b3d7a8708bc6f674a4cd9844bd6fe9"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae5481e1cee8403b1e3ba36df41cc5e20"><td class="memItemLeft" align="right" valign="top"><a id="ae5481e1cee8403b1e3ba36df41cc5e20"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><b>compute</b> (std::vector&lt; <a class="el" href="classpcl_1_1filters_1_1_pyramid.html">Pyramid</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">pcl::PointXYZRGBA</a> &gt;::PointCloudPtr &gt; &amp;output)</td></tr>
<tr class="separator:ae5481e1cee8403b1e3ba36df41cc5e20"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a999c53f1d50de0fd6b1955a2ddc122a9"><td class="memItemLeft" align="right" valign="top"><a id="a999c53f1d50de0fd6b1955a2ddc122a9"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><b>compute</b> (std::vector&lt; <a class="el" href="classpcl_1_1filters_1_1_pyramid.html">Pyramid</a>&lt; <a class="el" href="structpcl_1_1_r_g_b.html">pcl::RGB</a> &gt;::PointCloudPtr &gt; &amp;output)</td></tr>
<tr class="separator:a999c53f1d50de0fd6b1955a2ddc122a9"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-methods"></a>
Private 成员函数</h2></td></tr>
<tr class="memitem:a7f125d5f50904617d893a9381f27c0f7"><td class="memItemLeft" align="right" valign="top"><a id="a7f125d5f50904617d893a9381f27c0f7"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#a7f125d5f50904617d893a9381f27c0f7">initCompute</a> ()</td></tr>
<tr class="memdesc:a7f125d5f50904617d893a9381f27c0f7"><td class="mdescLeft">&#160;</td><td class="mdescRight">init computation <br /></td></tr>
<tr class="separator:a7f125d5f50904617d893a9381f27c0f7"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afdcb18098b44b1c45fc51850404a9284"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#afdcb18098b44b1c45fc51850404a9284">nullify</a> (<a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;p) const</td></tr>
<tr class="memdesc:afdcb18098b44b1c45fc51850404a9284"><td class="mdescLeft">&#160;</td><td class="mdescRight">nullify a point  <a href="classpcl_1_1filters_1_1_pyramid.html#afdcb18098b44b1c45fc51850404a9284">更多...</a><br /></td></tr>
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<tr class="memitem:abcca3b1ea43e6de156a888709ecc95ee"><td class="memItemLeft" align="right" valign="top"><a id="abcca3b1ea43e6de156a888709ecc95ee"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><b>nullify</b> (<a class="el" href="structpcl_1_1_r_g_b.html">pcl::RGB</a> &amp;p)</td></tr>
<tr class="separator:abcca3b1ea43e6de156a888709ecc95ee"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-attribs"></a>
Private 属性</h2></td></tr>
<tr class="memitem:a7f84159105826f392f69b4408078b2f3"><td class="memItemLeft" align="right" valign="top"><a id="a7f84159105826f392f69b4408078b2f3"></a>
PointCloudConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#a7f84159105826f392f69b4408078b2f3">input_</a></td></tr>
<tr class="memdesc:a7f84159105826f392f69b4408078b2f3"><td class="mdescLeft">&#160;</td><td class="mdescRight">The input point cloud dataset. <br /></td></tr>
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<tr class="memitem:a421e2d411172adc23ba76a79a6f5c725"><td class="memItemLeft" align="right" valign="top"><a id="a421e2d411172adc23ba76a79a6f5c725"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#a421e2d411172adc23ba76a79a6f5c725">levels_</a></td></tr>
<tr class="memdesc:a421e2d411172adc23ba76a79a6f5c725"><td class="mdescLeft">&#160;</td><td class="mdescRight">number of pyramid levels <br /></td></tr>
<tr class="separator:a421e2d411172adc23ba76a79a6f5c725"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a019163e3bae8de7652566f2e4d83ef91"><td class="memItemLeft" align="right" valign="top"><a id="a019163e3bae8de7652566f2e4d83ef91"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#a019163e3bae8de7652566f2e4d83ef91">large_</a></td></tr>
<tr class="memdesc:a019163e3bae8de7652566f2e4d83ef91"><td class="mdescLeft">&#160;</td><td class="mdescRight">use large smoothing kernel <br /></td></tr>
<tr class="separator:a019163e3bae8de7652566f2e4d83ef91"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad4533d28b1966832730d3fa68881dfea"><td class="memItemLeft" align="right" valign="top"><a id="ad4533d28b1966832730d3fa68881dfea"></a>
std::string&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#ad4533d28b1966832730d3fa68881dfea">name_</a></td></tr>
<tr class="memdesc:ad4533d28b1966832730d3fa68881dfea"><td class="mdescLeft">&#160;</td><td class="mdescRight">filter name <br /></td></tr>
<tr class="separator:ad4533d28b1966832730d3fa68881dfea"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af16363aa8b728ccbb6929625f5d19fe7"><td class="memItemLeft" align="right" valign="top"><a id="af16363aa8b728ccbb6929625f5d19fe7"></a>
Eigen::MatrixXf&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#af16363aa8b728ccbb6929625f5d19fe7">kernel_</a></td></tr>
<tr class="memdesc:af16363aa8b728ccbb6929625f5d19fe7"><td class="mdescLeft">&#160;</td><td class="mdescRight">smoothing kernel <br /></td></tr>
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<tr class="memitem:ae9684a8a0e8b8c351f5dc7c134b431b0"><td class="memItemLeft" align="right" valign="top"><a id="ae9684a8a0e8b8c351f5dc7c134b431b0"></a>
float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#ae9684a8a0e8b8c351f5dc7c134b431b0">threshold_</a></td></tr>
<tr class="memdesc:ae9684a8a0e8b8c351f5dc7c134b431b0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Threshold distance between adjacent points <br /></td></tr>
<tr class="separator:ae9684a8a0e8b8c351f5dc7c134b431b0"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afa6680c314b3e9d07e79a783986453bc"><td class="memItemLeft" align="right" valign="top"><a id="afa6680c314b3e9d07e79a783986453bc"></a>
unsigned int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1filters_1_1_pyramid.html#afa6680c314b3e9d07e79a783986453bc">threads_</a></td></tr>
<tr class="memdesc:afa6680c314b3e9d07e79a783986453bc"><td class="mdescLeft">&#160;</td><td class="mdescRight">number of threads <br /></td></tr>
<tr class="separator:afa6680c314b3e9d07e79a783986453bc"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><h3>template&lt;typename PointT&gt;<br />
class pcl::filters::Pyramid&lt; PointT &gt;</h3>

<p><a class="el" href="classpcl_1_1filters_1_1_pyramid.html">Pyramid</a> constructs a multi-scale representation of an organised point cloud. It is an iterative smoothing subsampling algorithm. The subsampling is fixed to 2. Two smoothing kernels may be used:</p><ul>
<li>[1/16 1/4 3/8 1/4 1/16] slower but produces finer result;</li>
<li>[1/4 1/2 1/2] the more conventional binomial kernel which is faster. We use a memory efficient algorithm so the convolving and subsampling are combined in a single step.</li>
</ul>
<dl class="section author"><dt>作者</dt><dd>Nizar Sallem </dd></dl>
</div><h2 class="groupheader">成员函数说明</h2>
<a id="a1685b0b45e15390522d88c1914fbcfb8"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a1685b0b45e15390522d88c1914fbcfb8">&#9670;&nbsp;</a></span>compute()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1filters_1_1_pyramid.html">pcl::filters::Pyramid</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::compute </td>
          <td>(</td>
          <td class="paramtype">std::vector&lt; PointCloudPtr &gt; &amp;&#160;</td>
          <td class="paramname"><em>output</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>compute the pyramid levels. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>the constructed pyramid. It is resized to the number of levels. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section remark"><dt>备注</dt><dd>input_ is copied to output[0] for consistency reasons. </dd></dl>
<div class="fragment"><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;{</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;  std::cout &lt;&lt; <span class="stringliteral">&quot;compute&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1filters_1_1_pyramid.html#a7f125d5f50904617d893a9381f27c0f7">initCompute</a> ())</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;  {</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::%s::compute] initCompute failed!\n&quot;</span>, getClassName ().c_str ());</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;  }</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160; </div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;  <span class="keywordtype">int</span> kernel_rows = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (<a class="code" href="classpcl_1_1filters_1_1_pyramid.html#af16363aa8b728ccbb6929625f5d19fe7">kernel_</a>.rows ());</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;  <span class="keywordtype">int</span> kernel_cols = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (<a class="code" href="classpcl_1_1filters_1_1_pyramid.html#af16363aa8b728ccbb6929625f5d19fe7">kernel_</a>.cols ());</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;  <span class="keywordtype">int</span> kernel_center_x = kernel_cols / 2;</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;  <span class="keywordtype">int</span> kernel_center_y = kernel_rows / 2;</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160; </div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;  output.resize (<a class="code" href="classpcl_1_1filters_1_1_pyramid.html#a421e2d411172adc23ba76a79a6f5c725">levels_</a> + 1);</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;  output[0].reset (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointT&gt;</a>);</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;  *(output[0]) = *<a class="code" href="classpcl_1_1filters_1_1_pyramid.html#a7f84159105826f392f69b4408078b2f3">input_</a>;</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160; </div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1filters_1_1_pyramid.html#a7f84159105826f392f69b4408078b2f3">input_</a>-&gt;is_dense)</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;  {</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> l = 1; l &lt;= <a class="code" href="classpcl_1_1filters_1_1_pyramid.html#a421e2d411172adc23ba76a79a6f5c725">levels_</a>; ++l)</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    {</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;      output[l].reset (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointT&gt;</a> (output[l-1]-&gt;width/2, output[l-1]-&gt;height/2));</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;      <span class="keyword">const</span> <a class="code" href="classpcl_1_1_point_cloud.html">PointCloud&lt;PointT&gt;</a> &amp;previous = *output[l-1];</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;      <a class="code" href="classpcl_1_1_point_cloud.html">PointCloud&lt;PointT&gt;</a> &amp;next = *output[l];</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;<span class="preprocessor">#ifdef _OPENMP</span></div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;<span class="preprocessor">#pragma omp parallel for shared (next) num_threads(threads_)</span></div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;      <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i=0; i &lt; next.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a>; ++i)</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;      {</div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;        <span class="keywordflow">for</span>(<span class="keywordtype">int</span> j=0; j &lt; next.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a>; ++j)</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;        {</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;          <span class="keywordflow">for</span>(<span class="keywordtype">int</span> m=0; m &lt; kernel_rows; ++m)</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;          {</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;            <span class="keywordtype">int</span> mm = kernel_rows - 1 - m;      </div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;            <span class="keywordflow">for</span>(<span class="keywordtype">int</span> n=0; n &lt; kernel_cols; ++n) </div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;            {</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;              <span class="keywordtype">int</span> nn = kernel_cols - 1 - n;</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160; </div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;              <span class="keywordtype">int</span> ii = 2*i + (m - kernel_center_y);</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;              <span class="keywordtype">int</span> jj = 2*j + (n - kernel_center_x);</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;              </div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;              <span class="keywordflow">if</span> (ii &lt; 0) ii = 0;</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;              <span class="keywordflow">if</span> (ii &gt;= previous.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a>) ii = previous.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> - 1;</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;              <span class="keywordflow">if</span> (jj &lt; 0) jj = 0;</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;              <span class="keywordflow">if</span> (jj &gt;= previous.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a>) jj = previous.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> - 1;</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;              next.<a class="code" href="classpcl_1_1_point_cloud.html#a1155fe4ba5cdc7e83cb72159a4ea02dc">at</a> (j,i) += previous.<a class="code" href="classpcl_1_1_point_cloud.html#a1155fe4ba5cdc7e83cb72159a4ea02dc">at</a> (jj,ii) * <a class="code" href="classpcl_1_1filters_1_1_pyramid.html#af16363aa8b728ccbb6929625f5d19fe7">kernel_</a> (mm,nn);</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;            }</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;          }</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;        }</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;      }</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;    }</div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;  }</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;  {</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> l = 1; l &lt;= <a class="code" href="classpcl_1_1filters_1_1_pyramid.html#a421e2d411172adc23ba76a79a6f5c725">levels_</a>; ++l)</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    {</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;      output[l].reset (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointT&gt;</a> (output[l-1]-&gt;width/2, output[l-1]-&gt;height/2));</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;      <span class="keyword">const</span> <a class="code" href="classpcl_1_1_point_cloud.html">PointCloud&lt;PointT&gt;</a> &amp;previous = *output[l-1];</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;      <a class="code" href="classpcl_1_1_point_cloud.html">PointCloud&lt;PointT&gt;</a> &amp;next = *output[l];</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;<span class="preprocessor">#ifdef _OPENMP</span></div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;<span class="preprocessor">#pragma omp parallel for shared (next) num_threads(threads_)</span></div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;      <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i=0; i &lt; next.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a>; ++i)</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;      {</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;        <span class="keywordflow">for</span>(<span class="keywordtype">int</span> j=0; j &lt; next.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a>; ++j)</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;        {</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;          <span class="keywordtype">float</span> weight = 0;</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;          <span class="keywordflow">for</span>(<span class="keywordtype">int</span> m=0; m &lt; kernel_rows; ++m)</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;          {</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;            <span class="keywordtype">int</span> mm = kernel_rows - 1 - m;</div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;            <span class="keywordflow">for</span>(<span class="keywordtype">int</span> n=0; n &lt; kernel_cols; ++n)</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;            {</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;              <span class="keywordtype">int</span> nn = kernel_cols - 1 - n;</div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;              <span class="keywordtype">int</span> ii = 2*i + (m - kernel_center_y);</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;              <span class="keywordtype">int</span> jj = 2*j + (n - kernel_center_x);</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;              <span class="keywordflow">if</span> (ii &lt; 0) ii = 0;</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;              <span class="keywordflow">if</span> (ii &gt;= previous.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a>) ii = previous.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> - 1;</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;              <span class="keywordflow">if</span> (jj &lt; 0) jj = 0;</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;              <span class="keywordflow">if</span> (jj &gt;= previous.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a>) jj = previous.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> - 1;</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;              <span class="keywordflow">if</span> (!isFinite (previous.<a class="code" href="classpcl_1_1_point_cloud.html#a1155fe4ba5cdc7e83cb72159a4ea02dc">at</a> (jj,ii)))</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;                <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;              <span class="keywordflow">if</span> (pcl::squaredEuclideanDistance (previous.<a class="code" href="classpcl_1_1_point_cloud.html#a1155fe4ba5cdc7e83cb72159a4ea02dc">at</a> (2*j,2*i), previous.<a class="code" href="classpcl_1_1_point_cloud.html#a1155fe4ba5cdc7e83cb72159a4ea02dc">at</a> (jj,ii)) &lt; <a class="code" href="classpcl_1_1filters_1_1_pyramid.html#ae9684a8a0e8b8c351f5dc7c134b431b0">threshold_</a>)</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;              {</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;                next.<a class="code" href="classpcl_1_1_point_cloud.html#a1155fe4ba5cdc7e83cb72159a4ea02dc">at</a> (j,i) += previous.<a class="code" href="classpcl_1_1_point_cloud.html#a1155fe4ba5cdc7e83cb72159a4ea02dc">at</a> (jj,ii).x * <a class="code" href="classpcl_1_1filters_1_1_pyramid.html#af16363aa8b728ccbb6929625f5d19fe7">kernel_</a> (mm,nn);</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;                weight+= <a class="code" href="classpcl_1_1filters_1_1_pyramid.html#af16363aa8b728ccbb6929625f5d19fe7">kernel_</a> (mm,nn);</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;              }</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;            }</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;          }</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;          <span class="keywordflow">if</span> (weight == 0)</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;            <a class="code" href="classpcl_1_1filters_1_1_pyramid.html#afdcb18098b44b1c45fc51850404a9284">nullify</a> (next.<a class="code" href="classpcl_1_1_point_cloud.html#a1155fe4ba5cdc7e83cb72159a4ea02dc">at</a> (j,i));</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;          <span class="keywordflow">else</span></div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;          {</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;            weight = 1.f/weight;</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;            next.<a class="code" href="classpcl_1_1_point_cloud.html#a1155fe4ba5cdc7e83cb72159a4ea02dc">at</a> (j,i)*= weight;</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;          }</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;        }</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;      }</div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;    }</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;  }    </div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html"><div class="ttname"><a href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a></div><div class="ttdoc">PointCloud represents the base class in PCL for storing collections of 3D points.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:173</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a1155fe4ba5cdc7e83cb72159a4ea02dc"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a1155fe4ba5cdc7e83cb72159a4ea02dc">pcl::PointCloud::at</a></div><div class="ttdeci">const PointT &amp; at(int column, int row) const</div><div class="ttdoc">Obtain the point given by the (column, row) coordinates. Only works on organized datasets (those that...</div><div class="ttdef"><b>Definition:</b> point_cloud.h:283</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a2185a6453f8ad905d7bdf7b45754a160"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">pcl::PointCloud::width</a></div><div class="ttdeci">uint32_t width</div><div class="ttdoc">The point cloud width (if organized as an image-structure).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:413</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a4f34b45220c57f96607513ffad0d9582"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">pcl::PointCloud::height</a></div><div class="ttdeci">uint32_t height</div><div class="ttdoc">The point cloud height (if organized as an image-structure).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:415</div></div>
<div class="ttc" id="aclasspcl_1_1filters_1_1_pyramid_html_a421e2d411172adc23ba76a79a6f5c725"><div class="ttname"><a href="classpcl_1_1filters_1_1_pyramid.html#a421e2d411172adc23ba76a79a6f5c725">pcl::filters::Pyramid::levels_</a></div><div class="ttdeci">int levels_</div><div class="ttdoc">number of pyramid levels</div><div class="ttdef"><b>Definition:</b> pyramid.h:151</div></div>
<div class="ttc" id="aclasspcl_1_1filters_1_1_pyramid_html_a7f125d5f50904617d893a9381f27c0f7"><div class="ttname"><a href="classpcl_1_1filters_1_1_pyramid.html#a7f125d5f50904617d893a9381f27c0f7">pcl::filters::Pyramid::initCompute</a></div><div class="ttdeci">bool initCompute()</div><div class="ttdoc">init computation</div><div class="ttdef"><b>Definition:</b> pyramid.hpp:44</div></div>
<div class="ttc" id="aclasspcl_1_1filters_1_1_pyramid_html_a7f84159105826f392f69b4408078b2f3"><div class="ttname"><a href="classpcl_1_1filters_1_1_pyramid.html#a7f84159105826f392f69b4408078b2f3">pcl::filters::Pyramid::input_</a></div><div class="ttdeci">PointCloudConstPtr input_</div><div class="ttdoc">The input point cloud dataset.</div><div class="ttdef"><b>Definition:</b> pyramid.h:149</div></div>
<div class="ttc" id="aclasspcl_1_1filters_1_1_pyramid_html_ae9684a8a0e8b8c351f5dc7c134b431b0"><div class="ttname"><a href="classpcl_1_1filters_1_1_pyramid.html#ae9684a8a0e8b8c351f5dc7c134b431b0">pcl::filters::Pyramid::threshold_</a></div><div class="ttdeci">float threshold_</div><div class="ttdoc">Threshold distance between adjacent points</div><div class="ttdef"><b>Definition:</b> pyramid.h:159</div></div>
<div class="ttc" id="aclasspcl_1_1filters_1_1_pyramid_html_af16363aa8b728ccbb6929625f5d19fe7"><div class="ttname"><a href="classpcl_1_1filters_1_1_pyramid.html#af16363aa8b728ccbb6929625f5d19fe7">pcl::filters::Pyramid::kernel_</a></div><div class="ttdeci">Eigen::MatrixXf kernel_</div><div class="ttdoc">smoothing kernel</div><div class="ttdef"><b>Definition:</b> pyramid.h:157</div></div>
<div class="ttc" id="aclasspcl_1_1filters_1_1_pyramid_html_afdcb18098b44b1c45fc51850404a9284"><div class="ttname"><a href="classpcl_1_1filters_1_1_pyramid.html#afdcb18098b44b1c45fc51850404a9284">pcl::filters::Pyramid::nullify</a></div><div class="ttdeci">void nullify(PointT &amp;p) const</div><div class="ttdoc">nullify a point</div><div class="ttdef"><b>Definition:</b> pyramid.h:143</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a32e8b1516818794aabad33ab343cdca5">&#9670;&nbsp;</a></span>getDistanceThreshold()</h2>

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<dl class="section return"><dt>返回</dt><dd>the distance threshold </dd></dl>
<div class="fragment"><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;{ <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1filters_1_1_pyramid.html#ae9684a8a0e8b8c351f5dc7c134b431b0">threshold_</a>); }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a0f827772542d5fa4df7210608972f433">&#9670;&nbsp;</a></span>getNumberOfLevels()</h2>

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<dl class="section return"><dt>返回</dt><dd>the number of pyramid levels </dd></dl>
<div class="fragment"><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;{ <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1filters_1_1_pyramid.html#a421e2d411172adc23ba76a79a6f5c725">levels_</a>); }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#afdcb18098b44b1c45fc51850404a9284">&#9670;&nbsp;</a></span>nullify()</h2>

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          <td>(</td>
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<p>nullify a point </p>
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<div class="fragment"><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;        {</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;          p.x = p.y = p.z = std::numeric_limits&lt;float&gt;::quiet_NaN ();</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;        }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a69d206f5d132cc93c8ec8889f2106d04">&#9670;&nbsp;</a></span>setDistanceThreshold()</h2>

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<p>Only points such us distance (center,point) &lt; threshold are accounted for to prevent ghost points. Default value is 0.01, to disable set to std::numeric&lt;float&gt;::infinity (). </p><dl class="params"><dt>参数</dt><dd>
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<div class="fragment"><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;{ <a class="code" href="classpcl_1_1filters_1_1_pyramid.html#ae9684a8a0e8b8c351f5dc7c134b431b0">threshold_</a> = threshold; }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#aa05721343da8b52426dcb1b704452e02">&#9670;&nbsp;</a></span>setInputCloud()</h2>

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<p>Provide a pointer to the input dataset </p>
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    <tr><td class="paramname">cloud</td><td>the const boost shared pointer to a <a class="el" href="classpcl_1_1_point_cloud.html" title="PointCloud represents the base class in PCL for storing collections of 3D points.">PointCloud</a> message </td></tr>
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<div class="fragment"><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;{ <a class="code" href="classpcl_1_1filters_1_1_pyramid.html#a7f84159105826f392f69b4408078b2f3">input_</a> = cloud; }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a85795a7ea853143678a8ad02a2aae296">&#9670;&nbsp;</a></span>setLargeSmoothingKernel()</h2>

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<p>Choose a larger smoothing kernel for enhanced smoothing. </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramname">large</td><td>if true large smoothng kernel will be used. </td></tr>
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<div class="fragment"><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;{ <a class="code" href="classpcl_1_1filters_1_1_pyramid.html#a019163e3bae8de7652566f2e4d83ef91">large_</a> = large; }</div>
<div class="ttc" id="aclasspcl_1_1filters_1_1_pyramid_html_a019163e3bae8de7652566f2e4d83ef91"><div class="ttname"><a href="classpcl_1_1filters_1_1_pyramid.html#a019163e3bae8de7652566f2e4d83ef91">pcl::filters::Pyramid::large_</a></div><div class="ttdeci">bool large_</div><div class="ttdoc">use large smoothing kernel</div><div class="ttdef"><b>Definition:</b> pyramid.h:153</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a1421311278e5d90a4cb936a88ff79cb8">&#9670;&nbsp;</a></span>setNumberOfLevels()</h2>

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<p>Set the number of pyramid levels </p>
<dl class="params"><dt>参数</dt><dd>
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<div class="fragment"><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;{ <a class="code" href="classpcl_1_1filters_1_1_pyramid.html#a421e2d411172adc23ba76a79a6f5c725">levels_</a> = levels; }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a708555045622338b43cf7a1439ce6d03">&#9670;&nbsp;</a></span>setNumberOfThreads()</h2>

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<p>Initialize the scheduler and set the number of threads to use. </p>
<dl class="params"><dt>参数</dt><dd>
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<div class="fragment"><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;{ <a class="code" href="classpcl_1_1filters_1_1_pyramid.html#afa6680c314b3e9d07e79a783986453bc">threads_</a> = nr_threads; }</div>
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